SoroushVahidi/scidocs huggingface dataset:
Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition
(2014)

Domain adaptation aims to correct the mismatch in statistical properties between the source domain on which a classifier is trained and the target domain to which the classifier is to be applied. In this paper, we address the challenging scenario of unsupervised domain adaptation, where the target domain does not provide any annotated data to assist in adapting the classifier. Our strategy is to learn robust features which are resilient to the mismatch across domains and then use them to construct classifiers that will perform well on the target domain. To this end, we propose novel kernel learning approaches to infer such features for adaptation. Concretely, we explore two closely related directions. In the first direction, we propose unsupervised learning of a geodesic flow kernel (GFK). The GFK summarizes the inner products in an infinite sequence of feature subspaces that smoothly interpolates between the source and target domains. In the second direction, we propose supervised learning of a kernel that discriminatively combines multiple base GFKs. Those base kernels model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks—the most useful data instances that reveal the similarity between the source and the target domains, thus bridging them to achieve adaptation. Our approaches are computationally convenient, automatically infer important hyper-parameters, and are capable of learning features and classifiers discriminatively without demanding labeled data from the target domain. In extensive empirical studies on standard benchmark recognition datasets, our appraches yield state-of-the-art results compared to a variety of competing methods.
Dissecting Contributions of Prefrontal Cortex and Fusiform Face Area to Face Working Memory
(2003)

Interactions between prefrontal cortex (PFC) and stimulusspecific visual cortical association areas are hypothesized to mediate visual working memory in behaving monkeys. To clarify the roles for homologous regions in humans, event-related fMRI was used to assess neural activity in PFC and fusiform face area (FFA) of subjects performing a delay-recognition task for faces. In both PFC and FFA, activity increased parametrically with memory load during encoding and maintenance of face stimuli, despite quantitative differences in the magnitude of activation. Moreover, timing differences in PFC and FFA activation during memory encoding and retrieval implied a context dependence in the flow of neural information. These results support existing neurophysiological models of visual working memory developed in the nonhuman primate.
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
(2014)

It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as dataintensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing. 2014 Elsevier Inc. All rights reserved.
Data quality management, data usage experience and acquisition intention of big data analytics
(2014)

Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics
The 8 requirements of real-time stream processing
(2005)

Applications that require real-time processing of high-volume data steams are pushing the limits of traditional data processing infrastructures. These stream-based applications include market feed processing and electronic trading on Wall Street, network and infrastructure monitoring, fraud detection, and command and control in military environments. Furthermore, as the "sea change" caused by cheap micro-sensor technology takes hold, we expect to see everything of material significance on the planet get "sensor-tagged" and report its state or location in real time. This sensorization of the real world will lead to a "green field" of novel monitoring and control applications with high-volume and low-latency processing requirements.Recently, several technologies have emerged---including off-the-shelf stream processing engines---specifically to address the challenges of processing high-volume, real-time data without requiring the use of custom code. At the same time, some existing software technologies, such as main memory DBMSs and rule engines, are also being "repurposed" by marketing departments to address these applications.In this paper, we outline eight requirements that a system software should meet to excel at a variety of real-time stream processing applications. Our goal is to provide high-level guidance to information technologists so that they will know what to look for when evaluation alternative stream processing solutions. As such, this paper serves a purpose comparable to the requirements papers in relational DBMSs and on-line analytical processing. We also briefly review alternative system software technologies in the context of our requirements.The paper attempts to be vendor neutral, so no specific commercial products are mentioned.
Data privacy through optimal k-anonymization
(2005)

Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a non-trivial dataset under a general model of the problem.
Privacy-preserving trajectory data publishing by local suppression
(2013)

The pervasiveness of location-aware devices has spawned extensive research in trajectory data mining, resulting in many important real-life applications. Yet, the privacy issue in sharing trajectory data among different parties often creates an obstacle for effective data mining. In this paper, we study the challenges of anonymizing trajectory data: high dimensionality, sparseness, and sequentiality. Employing traditional privacy models and anonymization methods often leads to low data utility in the resulting data and ineffective data mining. In addressing these challenges, this is the first paper to introduce local suppression to achieve a tailored privacy model for trajectory data anonymization. The framework allows the adoption of various data utility metrics for different data mining tasks. As an illustration, we aim at preserving both instances of location-time doublets and frequent sequences in a trajectory database, both being the foundation of many trajectory data ∗Corresponding author Email addresses: ru_che@encs.concordia.ca (Rui Chen), fung@ciise.concordia.ca (Benjamin C. M. Fung), no_moham@encs.concordia.ca (Noman Mohammed), bcdesai@cs.concordia.ca (Bipin C. Desai) Preprint submitted to Information Sciences April 10, 2011 mining tasks. Our experiments on both synthetic and real-life data sets suggest that the framework is effective and efficient to overcome the challenges in trajectory data anonymization. In particular, compared with the previous works in the literature, our proposed local suppression method can significantly improve the data utility in anonymous trajectory data.
Scholarly use of information: graduate students' information seeking behaviour
(2006)

Introduction. This study explored graduate students' information behaviour related to their process of inquiry and scholarly activities. Method. In depth, semi-structured interviews were conducted with one hundred graduate students representing all disciplines and departments from Carnegie Mellon University. Analysis. Working in pairs, we coded transcripts of interviews into meaningful categories using ATLAS.ti software. The combined use of quantitative and qualitative analysis aimed to reduce subjectivity. Results. Graduate students often begin with a meeting with professors who provide direction, recommend and provide resources. Other students help to shape graduate students' research activities, and university library personnel provide guidance in finding resources. The Internet plays a major role, although students continue to use print resources. Convenience, lack of sophistication in finding and using resources and course requirements affect their information behaviour. Findings vary across disciplines and between programmes. Conclusion. Libraries can influence students' information behaviour by re-evaluating their instructional programmes and provision of resources and services. They can take a lead by working with academic staff to guide students.
Music and language side by side in the brain: a PET study of the generation of melodies and sentences.
(2006)

Parallel generational tasks for music and language were compared using positron emission tomography. Amateur musicians vocally improvised melodic or linguistic phrases in response to unfamiliar, auditorily presented melodies or phrases. Core areas for generating melodic phrases appeared to be in left Brodmann area (BA) 45, right BA 44, bilateral temporal planum polare, lateral BA 6, and pre-SMA. Core areas for generating sentences seemed to be in bilateral posterior superior and middle temporal cortex (BA 22, 21), left BA 39, bilateral superior frontal (BA 8, 9), left inferior frontal (BA 44, 45), anterior cingulate, and pre-SMA. Direct comparisons of the two tasks revealed activations in nearly identical functional brain areas, including the primary motor cortex, supplementary motor area, Broca's area, anterior insula, primary and secondary auditory cortices, temporal pole, basal ganglia, ventral thalamus, and posterior cerebellum. Most of the differences between melodic and sentential generation were seen in lateralization tendencies, with the language task favouring the left hemisphere. However, many of the activations for each modality were bilateral, and so there was significant overlap. While clarification of this overlapping activity awaits higher-resolution measurements and interventional assessments, plausible accounts for it include component sharing, interleaved representations, and adaptive coding. With these and related findings, we outline a comparative model of shared, parallel, and distinctive features of the neural systems supporting music and language. The model assumes that music and language show parallel combinatoric generativity for complex sound structures (phonology) but distinctly different informational content (semantics).
Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters
(2006)

Although molecular dynamics (MD) simulations of biomolecular systems often run for days to months, many events of great scientific interest and pharmaceutical relevance occur on long time scales that remain beyond reach. We present several new algorithms and implementation techniques that significantly accelerate parallel MD simulations compared with current state-of-the-art codes. These include a novel parallel decomposition method and message-passing techniques that reduce communication requirements, as well as novel communication primitives that further reduce communication time. We have also developed numerical techniques that maintain high accuracy while using single precision computation in order to exploit processor-level vector instructions. These methods are embodied in a newly developed MD code called Desmond that achieves unprecedented simulation throughput and parallel scalability on commodity clusters. Our results suggest that Desmond's parallel performance substantially surpasses that of any previously described code. For example, on a standard benchmark, Desmond's performance on a conventional Opteron cluster with 2K processors slightly exceeded the reported performance of IBM's Blue Gene/L machine with 32K processors running its Blue Matter MD code.
Cellular IP: a new approach to Internet host mobility
(1999)

This paper describes a new approach to Internet host mobility. We argue that by separating local and wide area mobility, the performance of existing mobile host protocols (e.g. Mobile IP) can be significantly improved. We propose Cellular IP, a new lightweight and robust protocol that is optimized to support local mobility but efficiently interworks with Mobile IP to provide wide area mobility support. Cellular IP shows great benefit in comparison to existing host mobility proposals for environments where mobile hosts migrate frequently, which we argue, will be the rule rather than the exception as Internet wireless access becomes ubiquitous. Cellular IP maintains distributed cache for location management and routing purposes. Distributed paging cache coarsely maintains the position of 'idle' mobile hosts in a service area. Cellular IP uses this paging cache to quickly and efficiently pinpoint 'idle' mobile hosts that wish to engage in 'active' communications. This approach is beneficial because it can accommodate a large number of users attached to the network without overloading the location management system. Distributed routing cache maintains the position of active mobile hosts in the service area and dynamically refreshes the routing state in response to the handoff of active mobile hosts. These distributed location management and routing algorithms lend themselves to a simple and low cost implementation of Internet host mobility requiring no new packet formats, encapsulations or address space allocation beyond what is present in IP.
Real-time pen-and-ink illustration of landscapes
(2006)

Non-photorealistic rendering has been proven to be particularly efficient in conveying and transmitting selected visual information. Our paper presents a NPR rendering pipeline that supports pen-and-ink illustration for, but not limited to, complex landscape scenes in real time. This encompasses a simplification framework using clustering which enables new approaches to efficient and coherent rendering of stylized silhouettes, hatching and abstract shading. Silhouette stylization is performed in image-space. This avoids explicit computation of connected lines. Further, coherent hatching of the tree foliage is performed using an approximate view-dependent parameterization computed on-the-fly within the same simplification framework. All NPR algorithms are integrated with photorealistic rendering, allowing seamless transition and combination between a variety of photorealistic and non-photorealistic drawing styles.
Planar Array Antennas with Travelling-Wave Excitation in Millimeter-Wave Band
(2010)

あらまし ミリ波の周波数帯において指向性走査可能な平面アンテナが望まれている.本論文では,ミリ波の 代表的な二つの平面アンテナである導波管アンテナとマイクロストリップアンテナについて,開発した平面アン テナを紹介する.ペンシルビームが必要な機械走査型アンテナに適した二次元導波管アレーアンテナと,複数の サブアレーの配列からなる電子走査方式のサブアレー平面アンテナ用に,導波管アレーアンテナとマイクロスト リップコムラインアンテナについて開発した例を示す.これらについて,各放射素子で反射特性を低減すること が,特性の向上・設計自由度の拡大に有効であることを示す.また,これらのアンテナについて低損失に給電す る構造を示すと同時に,片端から給電するよりも,中央から給電する方が,周波数帯域幅が広くなることを示す. 更に,これらのアンテナ実現のためのキーとなる進行波励振アレー設計技術について解説する. キーワード ミリ波,アレーアンテナ,平面アンテナ,導波管アンテナ,マイクロストリップアンテナ
Multiview Clustering via Adaptively Weighted Procrustes
(2018)

In this paper, we make a multiview extension of the spectral rotation technique raised in single view spectral clustering research. Since spectral rotation is closely related to the Procrustes Analysis for points matching, we point out that classical Procrustes Average approach can be used for multiview clustering. Besides, we show that direct applying Procrustes Average (PA) in multiview tasks may not be optimal theoretically and empirically, since it does not take the clustering capacity differences of different views into consideration. Other than that, we propose an Adaptively Weighted Procrustes (AWP) approach to overcome the aforementioned deficiency. Our new AWP weights views with their clustering capacities and forms a weighted Procrustes Average problem accordingly. The optimization algorithm to solve the new model is computational complexity analyzed and convergence guaranteed. Experiments on five real-world datasets demonstrate the effectiveness and efficiency of the new models.
Accelerating Comparative Genomics Workflows in a Distributed Environment with Optimized Data Partitioning
(2014)

The advent of new sequencing technology has generated massive amounts of biological data at unprecedented rates. High-throughput bioinformatics tools are required to keep pace with this. Here, we implement a workflow-based model for parallelizing the data intensive task of genome alignment and variant calling with BWA and GATK's Haplotype Caller. We explore different approaches of partitioning data and how each affect the run time. We observe granularity-based partitioning for BWA and alignment-based partitioning for Halo type Caller to be the optimal choices for the pipeline. We identify the various challenges encountered while developing such an application and provide an insight into addressing them. We report significant performance improvements, from 12 days to 4 hours, while running the BWA-GATK pipeline using 100 nodes for analyzing high-coverage oak tree data.
An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer.
(2009)

The aim of this investigation was to develop and test two artificial neural networks (ANN) to apply to physical activity data collected with a commonly used uniaxial accelerometer. The first ANN model estimated physical activity metabolic equivalents (METs), and the second ANN identified activity type. Subjects (n = 24 men and 24 women, mean age = 35 yr) completed a menu of activities that included sedentary, light, moderate, and vigorous intensities, and each activity was performed for 10 min. There were three different activity menus, and 20 participants completed each menu. Oxygen consumption (in ml x kg(-1) x min(-1)) was measured continuously, and the average of minutes 4-9 was used to represent the oxygen cost of each activity. To calculate METs, activity oxygen consumption was divided by 3.5 ml x kg(-1) x min(-1) (1 MET). Accelerometer data were collected second by second using the Actigraph model 7164. For the analysis, we used the distribution of counts (10th, 25th, 50th, 75th, and 90th percentiles of a minute's second-by-second counts) and temporal dynamics of counts (lag, one autocorrelation) as the accelerometer feature inputs to the ANN. To examine model performance, we used the leave-one-out cross-validation technique. The ANN prediction of METs root-mean-squared error was 1.22 METs (confidence interval: 1.14-1.30). For the prediction of activity type, the ANN correctly classified activity type 88.8% of the time (confidence interval: 86.4-91.2%). Activity types were low-level activities, locomotion, vigorous sports, and household activities/other activities. This novel approach of applying ANNs for processing Actigraph accelerometer data is promising and shows that we can successfully estimate activity METs and identify activity type using ANN analytic procedures.
Crafting a multi-task CNN for viewpoint estimation
(2016)

Convolutional Neural Networks (CNNs) were recently shown to provide state-of-theart results for object category viewpoint estimation. However different ways of formulating this problem have been proposed and the competing approaches have been explored with very different design choices. This paper presents a comparison of these approaches in a unified setting as well as a detailed analysis of the key factors that impact performance. Followingly, we present a new joint training method with the detection task and demonstrate its benefit. We also highlight the superiority of classification approaches over regression approaches, quantify the benefits of deeper architectures and extended training data, and demonstrate that synthetic data is beneficial even when using ImageNet training data. By combining all these elements, we demonstrate an improvement of approximately 5% mAVP over previous state-of-the-art results on the Pascal3D+ dataset [28]. In particular for their most challenging 24 view classification task we improve the results from 31.1% to 36.1% mAVP.
Analysis on Credit Card Fraud Detection Methods 1
(2014)

Due to the theatrical increase of fraud which results in loss of dollars worldwide each year, several modern techniques in detecting fraud are persistently evolved and applied to many business fields. Fraud detection involves monitoring the activities of populations of users in order to estimate, perceive or avoid undesirable behavior. Undesirable behavior is a broad term including delinquency, fraud, intrusion, and account defaulting. This paper presents a survey of current techniques used in credit card fraud detection and telecommunication fraud. The goal of this paper is to provide a comprehensive review of different techniques to detect fraud.